{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:SSFMAJY4JYK4OBJ32PCYBR5JL7","short_pith_number":"pith:SSFMAJY4","schema_version":"1.0","canonical_sha256":"948ac0271c4e15c7053bd3c580c7a95fe185ea7300cd0f09bf59fa5e5124158d","source":{"kind":"arxiv","id":"1303.2060","version":2},"attestation_state":"computed","paper":{"title":"Adaptive Non-myopic Quantizer Design for Target Tracking in Wireless Sensor Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Engin Masazade, Pramod K. Varshney, Sijia Liu, Xiaojing Shen","submitted_at":"2013-03-08T17:27:02Z","abstract_excerpt":"In this paper, we investigate the problem of nonmyopic (multi-step ahead) quantizer design for target tracking using a wireless sensor network. Adopting the alternative conditional posterior Cramer-Rao lower bound (A-CPCRLB) as the optimization metric, we theoretically show that this problem can be temporally decomposed over a certain time window. Based on sequential Monte-Carlo methods for tracking, i.e., particle filters, we design the local quantizer adaptively by solving a particlebased non-linear optimization problem which is well suited for the use of interior-point algorithm and easily "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1303.2060","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2013-03-08T17:27:02Z","cross_cats_sorted":[],"title_canon_sha256":"f00778d6474de67be8d9e4ef042265483939c15574256846909b9242c3327e39","abstract_canon_sha256":"2c912acbb3cc321d5e9ccac1809bac1132e9e81e0434acb7ca91332384b2fbe2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:26:22.415194Z","signature_b64":"ScfoAntLYkgoJjkpxYUrYhjez0GAL4ewo3dQ/ztp8U68RcqiZyT5RUqSugprhScWh9pGSZqeAPL3EGpJqqGJCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"948ac0271c4e15c7053bd3c580c7a95fe185ea7300cd0f09bf59fa5e5124158d","last_reissued_at":"2026-05-18T03:26:22.414616Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:26:22.414616Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adaptive Non-myopic Quantizer Design for Target Tracking in Wireless Sensor Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Engin Masazade, Pramod K. Varshney, Sijia Liu, Xiaojing Shen","submitted_at":"2013-03-08T17:27:02Z","abstract_excerpt":"In this paper, we investigate the problem of nonmyopic (multi-step ahead) quantizer design for target tracking using a wireless sensor network. Adopting the alternative conditional posterior Cramer-Rao lower bound (A-CPCRLB) as the optimization metric, we theoretically show that this problem can be temporally decomposed over a certain time window. Based on sequential Monte-Carlo methods for tracking, i.e., particle filters, we design the local quantizer adaptively by solving a particlebased non-linear optimization problem which is well suited for the use of interior-point algorithm and easily "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1303.2060","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1303.2060","created_at":"2026-05-18T03:26:22.414707+00:00"},{"alias_kind":"arxiv_version","alias_value":"1303.2060v2","created_at":"2026-05-18T03:26:22.414707+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1303.2060","created_at":"2026-05-18T03:26:22.414707+00:00"},{"alias_kind":"pith_short_12","alias_value":"SSFMAJY4JYK4","created_at":"2026-05-18T12:27:59.945178+00:00"},{"alias_kind":"pith_short_16","alias_value":"SSFMAJY4JYK4OBJ3","created_at":"2026-05-18T12:27:59.945178+00:00"},{"alias_kind":"pith_short_8","alias_value":"SSFMAJY4","created_at":"2026-05-18T12:27:59.945178+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SSFMAJY4JYK4OBJ32PCYBR5JL7","json":"https://pith.science/pith/SSFMAJY4JYK4OBJ32PCYBR5JL7.json","graph_json":"https://pith.science/api/pith-number/SSFMAJY4JYK4OBJ32PCYBR5JL7/graph.json","events_json":"https://pith.science/api/pith-number/SSFMAJY4JYK4OBJ32PCYBR5JL7/events.json","paper":"https://pith.science/paper/SSFMAJY4"},"agent_actions":{"view_html":"https://pith.science/pith/SSFMAJY4JYK4OBJ32PCYBR5JL7","download_json":"https://pith.science/pith/SSFMAJY4JYK4OBJ32PCYBR5JL7.json","view_paper":"https://pith.science/paper/SSFMAJY4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1303.2060&json=true","fetch_graph":"https://pith.science/api/pith-number/SSFMAJY4JYK4OBJ32PCYBR5JL7/graph.json","fetch_events":"https://pith.science/api/pith-number/SSFMAJY4JYK4OBJ32PCYBR5JL7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SSFMAJY4JYK4OBJ32PCYBR5JL7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SSFMAJY4JYK4OBJ32PCYBR5JL7/action/storage_attestation","attest_author":"https://pith.science/pith/SSFMAJY4JYK4OBJ32PCYBR5JL7/action/author_attestation","sign_citation":"https://pith.science/pith/SSFMAJY4JYK4OBJ32PCYBR5JL7/action/citation_signature","submit_replication":"https://pith.science/pith/SSFMAJY4JYK4OBJ32PCYBR5JL7/action/replication_record"}},"created_at":"2026-05-18T03:26:22.414707+00:00","updated_at":"2026-05-18T03:26:22.414707+00:00"}